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HSC commitment-associated epigenetic signature is prognostic in acute myeloid leukemia.

ABSTRACT: Acute myeloid leukemia (AML) is characterized by disruption of HSC and progenitor cell differentiation. Frequently, AML is associated with mutations in genes encoding epigenetic modifiers. We hypothesized that analysis of alterations in DNA methylation patterns during healthy HSC commitment and differentiation would yield epigenetic signatures that could be used to identify stage-specific prognostic subgroups of AML. We performed a nano HpaII-tiny-fragment-enrichment-by-ligation-mediated-PCR (nanoHELP) assay to compare genome-wide cytosine methylation profiles between highly purified human long-term HSC, short-term HSC, common myeloid progenitors, and megakaryocyte-erythrocyte progenitors. We observed that the most striking epigenetic changes occurred during the commitment of short-term HSC to common myeloid progenitors and these alterations were predominantly characterized by loss of methylation. We developed a metric of the HSC commitment–associated methylation pattern that proved to be highly prognostic of overall survival in 3 independent large AML patient cohorts, regardless of patient treatment and epigenetic mutations. Application of the epigenetic signature metric for AML prognosis was superior to evaluation of commitment-based gene expression signatures. Together, our data define a stem cell commitment–associated methylome that is independently prognostic of poorer overall survival in AML.

SUBMITTER: Bartholdy B

PROVIDER: S-EPMC3934187 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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HSC commitment-associated epigenetic signature is prognostic in acute myeloid leukemia.

Bartholdy Boris B Christopeit Maximilian M Will Britta B Mo Yongkai Y Barreyro Laura L Yu Yiting Y Bhagat Tushar D TD Okoye-Okafor Ujunwa C UC Todorova Tihomira I TI Greally John M JM Levine Ross L RL Melnick Ari A Verma Amit A Steidl Ulrich U

The Journal of clinical investigation 20140301 3

Acute myeloid leukemia (AML) is characterized by disruption of HSC and progenitor cell differentiation. Frequently, AML is associated with mutations in genes encoding epigenetic modifiers. We hypothesized that analysis of alterations in DNA methylation patterns during healthy HSC commitment and differentiation would yield epigenetic signatures that could be used to identify stage-specific prognostic subgroups of AML. We performed a nano HpaII-tiny-fragment-enrichment-by-ligation-mediated-PCR (na ...[more]

PMID: 24487588

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Project description:Background: Acute myeloid leukemia (AML) is a heterogeneous malignant disease. SLC25A1, the gene encoding mitochondrial carrier subfamily of solute carrier proteins, was reported to be overexpressed in certain solid tumors. However, its expression and value as prognostic marker has not been assessed in AML. Methods: We retrieved RNA profile and corresponding clinical data of AML patients from the Beat AML, TCGA, and TARGET databases (TARGET_AML). Patients in the TCGA cohort were well-grouped into two group based on SLC25A1 and differentially expressed genes were determined between the SLC25A1 high and low group. The expression of SLC25A1 was validated with clinical samples. The survival and apoptosis of two AML cell lines were analyzed with SLC25A1 inhibitor (CTPI-2) treatment. Cox and the least absolute shrinkage and selection operator (LASSO) regression analyses were applied to Beat AML database to identify SLC25A1-associated genes for the construction of a prognostic risk-scoring model. Survival analysis was performed by Kaplan-Meier and receiver operator characteristic curves. Results: Our analysis revealed that high expressed level of SLC25A1 in AML patients correlates with unfavorable prognosis. Moreover, SLC25A1 expression was positively associated with metabolism activity. We further demonstrated that the inhibition of SLC25A1 could inhibit the proliferation and increase the apoptosis of AML cells. In addition, a panel of SLC25A1-associated genes, was identified to construct a prognostic risk-scoring model. This SLC25A1-associated prognostic signature (SPS) is an independent risk factor with high area under curve (AUC) values of receiver operating characteristic (ROC) curves. A high SPS in leukemia patients is associated with poor survival. A Prognostic nomogram including the SPS and other clinical parameters, was constructed and its predictive efficiency was confirmed. Conclusion: We have successfully established a SPS prognostic model that predict outcome and risk stratification in AML. This risk model can be used as an independent biomarker to assess prognosis of AML.

Project description:BackgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.MethodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.ResultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.ConclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.

Project description:BackgroundAcute myeloid leukemia (AML) is a highly aggressive hematological malignancy characterized by extensive genetic abnormalities that might affect the prognosis and provide potential drug targets for treatment. Reprogramming of lipid metabolism plays important roles in tumorigenesis and progression and has been newly recognized a new hallmark of malignancy, and some related molecules in the signal pathways could be prognostic biomarkers and potential therapeutic targets for cancer treatment. However, the clinical value of lipid metabolism reprogramming in AML has not been systematically explored. In this study, we aim to explore the clinical value of lipid metabolism reprogramming and develop a prognostic risk signature for AML.MethodsWe implemented univariate Cox regression analysis to identify the prognosis-related lipid metabolism genes, and then performed LASSO analysis to develop the risk signature with six lipid metabolism-related genes (LDLRAP1, PNPLA6, DGKA, PLA2G4A, CBR1, and EBP). The risk scores of samples were calculated and divided into low- and high-risk groups by the median risk score.ResultsSurvival analysis showed the high-risk group hold the significantly poorer outcomes than the low-risk group. The signature was validated in the GEO datasets and displayed a robust prognostic value in the stratification analysis. Multivariate analysis revealed the signature was an independent prognostic factor for AML patients and could serve as a potential prognostic biomarker in clinical evaluation. Furthermore, the risk signature was also found to be closely related to immune landscape and immunotherapy response in AML.ConclusionsOverall, we conducted a comprehensive analysis of lipid metabolism in AML and constructed a risk signature with six genes related to lipid metabolism for the malignancy, prognosis, and immune landscape of AML, and our study might contribute to better understanding in the use of metabolites and metabolic pathways as the potential prognostic biomarkers and therapeutic targets for AML.

Project description:BackgroundThe tumor microenvironment (TME) has an essential role in tumorigenesis, progression, and therapeutic response in many cancers. Currently, the role of TME in acute myeloid leukemia (AML) is unclear. This study investigated the correlation between immune-related genes and prognosis in AML patients.MethodsTranscriptome RNA-Seq data for 151 AML samples were downloaded from TCGA database (https://portal.gdc.cancer.gov/), and the immune related genes (irgs) were selected from Immport database. Bioinformatics screening was used to identify irgs for AML, and genes with a critical role in the prognosis of AML were selected for further analysis. To confirm the prognostic role of irgs in AML, we undertook protein-protein interaction (PPI) network analysis of the top 30 interacting genes. We then investigated associations between immune cell infiltration and prognosis in AML patients. Immunohistochemistry was used to validate protein expression levels between AML and normal bone marrow samples. Analysis of the drug sensitivity of the selected gene was then performed.ResultsThe integrin lymphocyte function-associated antigen 1 (CD11A/CD18; ITGAL/ITGB2) was identified as the key immune-related gene that significantly influenced prognosis in AML patients. Overexpression of ITGB2 indicated poor prognosis in AML patients (P=0.007). Risk modeling indicated that a high-risk score led to poor outcomes (P=3.076e-08) in AML patients. The risk model showed accuracy for predicting prognosis in AML patients, with area under curve (AUC) at 1 year, 0.816; AUC at 3 years, 0.82; and AUC at 5 years, 0.875. In addition, we found that ITGB2 had a powerful influence on immune cell infiltration into AML TME. The results of immunohistochemistry showed that AML patients had significantly higher ITGB2 protein expression than normal samples. The AML patients were divided into 2 groups based on ITGB2 risk scores. Drug sensitivity test results indicated that the high-risk group was sensitive to cytarabine, axitinib, bosutinib, and docetaxel, but resistant to cisplatin and bortezomib.ConclusionsIn the present study, we found that ITGB2 may be able to serve as a biomarker for assessing prognosis and drug sensitivity in AML patients.

Dataset Information

HSC commitment-associated epigenetic signature is prognostic in acute myeloid leukemia.

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HSC commitment-associated epigenetic signature is prognostic in acute myeloid leukemia.

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